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2021-04-15Zeitschriftenartikel DOI: 10.18452/22985
Biotic Yield Losses in the Southern Amazon, Brazil: Making Use of Smartphone-Assisted Plant Disease Diagnosis Data
dc.contributor.authorHampf, Anna Claudia
dc.contributor.authorNendel, Claas
dc.contributor.authorStrey, Simone
dc.contributor.authorStrey, Robert
dc.date.accessioned2021-06-17T10:11:20Z
dc.date.available2021-06-17T10:11:20Z
dc.date.issued2021-04-15none
dc.date.updated2021-04-29T15:22:45Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/23652
dc.description.abstractPathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil’s largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app’s functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an “expert” version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectplant pathologyeng
dc.subjectanimal pestseng
dc.subjectpathogenseng
dc.subjectmachine learningeng
dc.subjectdigital image processingeng
dc.subjectdisease diagnosiseng
dc.subjectcrowdsourcingeng
dc.subjectcrop losseseng
dc.subject.ddc570 Biologienone
dc.titleBiotic Yield Losses in the Southern Amazon, Brazil: Making Use of Smartphone-Assisted Plant Disease Diagnosis Datanone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/23652-1
dc.identifier.doihttp://dx.doi.org/10.18452/22985
dc.type.versionpublishedVersionnone
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn1664-462X
dcterms.bibliographicCitation.doi10.3389/fpls.2021.621168none
dcterms.bibliographicCitation.journaltitleFrontiers in plant science : FPLSnone
dcterms.bibliographicCitation.volume12none
dcterms.bibliographicCitation.articlenumber621168none
dcterms.bibliographicCitation.originalpublishernameFrontiers Medianone
dcterms.bibliographicCitation.originalpublisherplaceLausannenone
bua.import.affiliationHampf, Anna C.; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Albrecht Daniel Thaer Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Berlin, Germanynone
bua.import.affiliationNendel, Claas; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germanynone
bua.import.affiliationStrey, Simone; Progressive Environmental and Agricultural Technologies (PEAT) GmbH, Hannover, Germanynone
bua.import.affiliationStrey, Robert; Progressive Environmental and Agricultural Technologies (PEAT) GmbH, Hannover, Germanynone
bua.departmentLebenswissenschaftliche Fakultätnone

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